Speaker: Dr. Jurgen DOORNIK (University of Oxford)
Date: 26 June 2019 (Wed)
Time: 3:30pm – 5:30pm
Venue: RLB303, Research Complex
Automatic general-to-specific selection of univariate econometric models is now well established and available in software. Extensions include saturation estimators, e.g. adding an impulse dummy for every observation to handle outliers. This seminar will provide an overview of the approach, and then consider extension of these procedures to the multivariate setting. The starting point is a vector autoregression, and the final stage can be a simultaneous equations model where the role of identification is considered. The aim is to obtain procedures that are relevant for empirical modelling.
The need for machine-assisted learning in econometrics:
- Developing good models is difficult.
- Working with economic data is difficult:
- approximate measurements subject to revisions on a system that is huge,
- evolving, intercorrelated, maybe nonlinear, and prone to abrupt shifts.
- Need models for policy as well as forecasting :
- Black-box models insufficient: need to understand,
- Nonlinearities of secondary importance.
- Proliferation of data: Big data:
- But is there a proliferation of insight?
General-to-specific model selection (Gets, ‘Hendry’ or ‘LSE’ methodology) largely driven by David Hendry (DHSY, PcGive, Alchemy, Dynamic Econometrics, …)
General-to-specific automatic model selection, developed methodology and algorithms to handle these challenges
Doornik, J. A. (2009). Autometrics.
In J. L. Castle and N. Shephard (Eds.), The Methodology and Practice of Econometrics: Festschrift in Honour of David F. Hendry. Oxford: Oxford University Press.
Empirical Model Discovery and Theory Evaluation Automatic Selection Methods in Econometrics By David F. Hendry and Jurgen A. Doornik
Published by the MIT Press
Doornik, J. A. and K. Juselius (2018).
CATS 3: Cointegration Analysis of Time Series in OxMetrics.
London: Timberlake Consultants Press.